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Impact of evergreening on patients and health insurance: a meta analysis and
reimbursement cost analysis of citalopram/escitalopram antidepressants
BMC Medicine 2012, 10:142 doi:10.1186/1741-7015-10-142
Ali A Alkhafaji (ali.alkhafaji@htd.aphp.fr)
Ludovic Trinquart (ludovic.trinquart@htd.aphp.fr)
Gabriel Baron (gabriel.baron@htd.aphp.fr)
Moise Desvarieux (md108@mail.cumc.columbia.edu)
Philippe Ravaud (philippe.ravaud@htd.aphp.fr)
ISSN 1741-7015
Article type Research article
Submission date 11 July 2012
Acceptance date 8 October 2012
Publication date 20 November 2012
Article URL http://www.biomedcentral.com/1741-7015/10/142
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{Research article}
Impact of evergreening on patients and health insurance: a meta analysis
and reimbursement cost analysis of citalopram/escitalopram
antidepressants
Ali A Alkhafaji 1,2 , Ludovic Trinquart 1,2,3,4 , Gabriel Baron 1,2 , Moïse Desvarieux 2,5,6
and Philippe Ravaud 1,2,3,4,5,6*
1 Centre d'Epidémiologie Clinique, Hôpital Hôtel-Dieu, Assistance Publique-
Hôpitaux de Paris, 1 place du parvis Notre Dame, Paris, 75004, France.
2 INSERM U738, Hôpital Hôtel-Dieu, Assistance Publique-Hôpitaux de Paris, 1
place du parvis Notre Dame, Paris, 75004, France.
3 Université Paris Descartes - Sorbonne Paris Cité, 12 Rue de l'École de Médecine
Paris, 75006, France.
4 French Cochrane Centre, Hôpital Hôtel-Dieu, 1 place du parvis Notre Dame, Paris,
75004, France.
5 EHESP School of Public Health, Avenue du Professeur Léon-Bernard, CS 74312,
Rennes, 35043, France.
6 Department of Epidemiology, Columbia University Mailman School of Public
Health, 722 West 168th Street, New York, NY 10032, USA.
* Corresponding author: Philippe Ravaud, philippe.ravaud@htd.aphp.fr
Email addresses:
AA: ali.alkhafaji@htd.aphp.fr
LT: ludovic.trinquart@htd.aphp.fr
1

GB: Gabriel.baron@htd.aphp.fr
MD: md108@mail.cumc.columbia.edu
2

Abstract
Background: “Evergreening” refers to the numerous strategies whereby owners of
pharmaceutical products use patent laws and minor drug modifications to extend
their monopoly privileges on the drug. We aimed to evaluate the impact of
evergreening through the case study of the antidepressant citalopram and its chiral
switch form escitalopram by evaluating treatment efficacy and acceptability for
patients, as well as health insurance costs for society.
Methods: To assess efficacy and acceptability, we performed meta-analyses for
efficacy and acceptability. We compared direct evidence (meta-analysis of results of
head-to-head trials) and indirect evidence (adjusted indirect comparison of results of
placebo-controlled trials). To assess health insurance costs, we analyzed individual
reimbursement data from a representative sample of the French National Health
Insurance Inter-regime Information System (SNIIR-AM) from 2003 to 2010, which
allowed for projecting these results to the whole SNIIR-AM population (53 million
people).
Results: In the meta-analysis of seven head-to-head trials (2,174 patients), efficacy
was significantly better for escitalopram than citalopram (combined odds ratio (OR)
1.60 (95% confidence interval 1.05 to 2.46)). However, for the adjusted indirect
comparison of 10 citalopram and 12 escitalopram placebo-controlled trials, 2,984
and 3,777 patients respectively, efficacy was similar for the two drug forms
(combined indirect OR 1.03 (0.82 to 1.30)). Because of the discrepancy, we could
not combine direct and indirect data (test of inconsistency, P = 0.07). A similar
discrepancy was found for treatment acceptability. The overall reimbursement cost
burden for the citalopram, escitalopram and its generic forms was 120.6 million
Euros in 2010, with 96.8 million Euros for escitalopram.
Conclusions: The clinical benefit of escitalopram versus citalopram remains
uncertain. In our case of evergreening, escitalopram represented a substantially high
proportion of the overall reimbursement cost burden as compared with citalopram
and the generic forms.
Keywords: Evergreening, Meta-analysis, Health Insurance Reimbursement,
Escitalopram, Citalopram, Chiral switch, French health information system
3

Introduction
Evergreening refers to owners of pharmaceutical products using numerous
strategies, such as patent laws and minor drug modifications, to extend their
monopoly privileges with their products [1-3]. Typically, these strategies are
developed before expiry of the patent of an original drug, usually a high-revenue
drug [4-6]. If they succeed, they result in an extension of the patent protection period
or a new patent for a minimally modified version of the drug.
A consequence of evergreening is delayed entry of generic drugs into the market
with extension of the original drug patent or competition between the patent-
protected minimally modified version of the drug and generic drugs [7]. This situation
might increase drug reimbursement costs by keeping the cheaper generic versions
completely or partly out of the market [8]. Pharmaceutical companies defend
evergreening practices and claim that revised formulas benefit patients (for example,
by improving adherence) and the drug industry (for example, by providing incentives
for companies to engage in incremental innovation) [9-11].
Minimal modifications used in evergreening include use of a different salt or
molecule as an additive to the main drug components, change in formulation,
modified release or change in route of administration [12, 13]. An enantiomer patent
is another form of evergreening based on a chiral switch (that is, from a chiral drug
developed as a racemic mixture to a single enantiomer) [14]. Single-enantiomer
drugs represent more than 50% of the top-selling 100 drugs worldwide [15].
A typical example of this strategy was the case of citalopram/escitalopram, two
antidepressants. The Lundbeck company’s patent on citalopram has run out in many
countries [15]. The company launched a single-enantiomer drug, escitalopram,
before the patent on the original drug expired and significantly increased its
advertising campaigns to promote the new form [16]. However, the clinical
superiority of escitalopram over citalopram is still debated [15]. Moreover, previous
evergreening’s societal burden analyses, dedicated to other evergreening examples,
were based on projections of market shares and health insurance reimbursement
costs [4, 17].
4

We aimed to evaluate the impact of evergreening citalopram with the chiral switch
form escitalopram on efficacy and treatment acceptability for patients, as well as
health insurance costs for society.
Methods
We investigated the relative efficacy and acceptability of citalopram and
escitalopram by performing meta-analyses for direct evidence (meta-analyses of
results of head-to-head trials) and indirect evidence (adjusted indirect comparison of
results of placebo-controlled trials). Health insurance costs were analyzed through
reimbursement data for citalopram, its generic forms and escitalopram from the
French national health insurance information system.
Assessment of relative efficacy of escitalopram and citalopram
Identification and selection of randomized controlled trials
We identified randomized controlled trials by systematically identifying reviews
published from 2000 to 2011 and trial results published from 2011 to 2012 [18], see
Section 1, Additional file 1.
Eligible reviews assessed the efficacy of citalopram or escitalopram in adults with
major depression based on randomized trials. We searched several bibliographical
databases for reviews published between January 2000 and March 2011, and four
repositories of national health technology agencies, as well as the FDA. See Section
1, Additional file 1.
Eligible randomized trials assessed acute treatment efficacy, which was defined
as eight-week treatment efficacy of citalopram versus escitalopram or citalopram
and/or escitalopram versus placebo in patients with major depression, see Section 1,
Additional file 1 for detailed selection criteria. First, we screened selected reviews
and listed all included trials. The eligibility of trials was assessed independently by
two reviewers, with disagreements resolved by consensus. Then, we searched for
trial results published from March 2011 to February 2012 in MEDLINE and EMBASE.
Finally, we searched for trial results in databases from Lundbeck and Forest
5

egistries [19, 20]. We also contacted Lundbeck/France for a list of clinical trials for
the two medications.
Outcome measures
We assessed acute treatment efficacy, which was defined as eight-week
treatment. When the depression outcome was measured at several timepoints, we
extracted outcome data at eight weeks. If not reported, we extracted outcome data
for the closest time points, ranging from 4 to 12 weeks [21, 22], see Sections 1 and
2, Additional file 1 for details about data extraction. We used outcome data for the
Montgomery-Åsberg depression rating scale (MADRS) and, if not reported, the
Hamilton scale. Efficacy was assessed by the proportion of responders in each
treatment group, defined as patients with a decrease in depression score from
baseline to follow-up of at least 50%, see Section 2, Additional file 1.
We assessed treatment acceptability by the proportion of patients who did not
drop out of the allocated treatment during the short-term treatment period
(completers), see Section 2, Additional file 1. Data were extracted by two reviewers
independently. Disagreements were resolved by discussion. If outcome data were
available from FDA reports and other sources, priority was given to FDA data,
because the FDA re-analyses of raw data from the sponsor adhered to the pre-
specified statistical methods in the trial protocol [23].
Meta-analysis of head-to-head trials and adjusted indirect comparisons of
placebo-controlled trials
First, we performed a meta-analysis of head-to-head trials. Then, we performed
an adjusted indirect evaluation of the relative efficacy of each treatment compared to
placebo using Bucher’s method [24]. The effect of treatment was measured by odds
ratios (ORs) and 95% confidence intervals (95% CIs). Combined estimates were
calculated by fixed- and random-effects models. The two models always showed
similar results [25]. In cases of significant treatment effect, we re-expressed the
results in terms of number needed to treat (NNT). We computed the NNT from the
combined ORs and by considering low and high response rates for the control group,
defined as the lower and upper bounds of the 95% CI for the combined response
rate across control groups in the meta-analysis.
6

We assessed heterogeneity of treatment effect estimates across trials using the I²
statistic. We assessed similarity (whether citalopram and escitalopram placebo-
controlled trials were similar for moderators of relative treatment effect) by comparing
clinical and methodological characteristics of randomized comparisons [26]. We
assessed inconsistency between the direct and adjusted indirect estimate by the
difference between the two estimates and associated 95% CIs and tested whether it
was statistically significant [27].
We assessed small-study effects by funnel plots [28]. Sensitivity analyses for
direct and adjusted indirect comparisons involved re-analyzing data after excluding
head-to-head trials without comparable dosages and excluding placebo-controlled
trials as soon as the treated group did not receive defined daily dose (DDD)
dosages, respectively. In addition, sensitivity analyses were performed excluding
trials with imputed outcome data and trials of older adults only.
Assessment of reimbursement costs for citalopram, its generic drugs and
escitalopram
To assess the reimbursement burden of the three drug forms on the French
general health insurance regimes, we analyzed data from the French national health
insurance information system (Système National d'Information Inter-Régimes de
l'Assurance Maladie, SNIIR-AM).
Data sources
The SNIIR-AM contains anonymous and comprehensive data on health spending
reimbursements from 2003. In 2009, it covered 86% of the French population,
approximately 53 million people. We used a random representative sample (1/97) of
SNIIR-AM, the Echantillon généraliste de bénéficiaires (EGB). In 2009, the EGB
consisted of about 500,000 beneficiaries [29, 30]. We searched the EGB database
using French identifiers for drug products for the three drug forms. We examined all
reimbursement claims for the drugs between January 2003 and December 2010,
given that generic citalopram was introduced in December 2003 and escitalopram
was introduced in June 2005 in France.
Reimbursement cost analysis
7

To illustrate changes in consumption pattern, we calculated the monthly number
of reimbursements and monthly consumption in DDD units for the three drug forms.
According to the World Health Organization, the DDD is the assumed average
maintenance dose per day for a drug used for its main indication in adults. The DDD
is 20 mg for citalopram and 10 mg for escitalopram [31]. To illustrate the evolution of
spending, we calculated the total monthly reimbursement costs for the three drug
forms. For all data, we produced time series plots. Because the EGB sample is
representative of the SNIIR-AM population, we projected our analyses by dividing all
results by the sampling fraction so that estimates reflected the whole SNIIR-AM
population [30].
Analyses involved use of Stata MP v10.0 (Stata Corp., College Station, TX, USA).
A P

Outcome assessment times ranged from 4 to 12 weeks. All trials were sponsored by
pharmaceutical companies, except one by the Chinese National Institute for
Pharmaceutical Research, (See Section 5, Additional file 1).
Meta-analysis of head-to-head trials
Of seven identified head-to-head randomized comparisons, all showed the
superiority of escitalopram over citalopram except Ou 2011 [32]. Escitalopram was
associated with higher response as compared with citalopram (random-effects
model, combined OR 1.60 (95% CI 1.05 to .46)) (See Section 8, Additional file 1).
This combined OR would translate to a NNT of 8.5 and 9.6 patients to achieve an
additional response with escitalopram compared to citalopram, when the control
response rate is lower (47%) or higher (61%). Heterogeneity was considerable
across trials (I² = 80%; τ² = 0.26), but mainly because of one trial, Yevtushenko
2007, which showed outlying results. The funnel plot of the seven comparisons did
not reveal asymmetry; see Section 9, Additional file 1.
Concerning acceptability, the proportion of treatment completers was greater with
escitalopram than citalopram (Section 10, Additional file 1). For escitalopram versus
citalopram, the random-effects combined OR was 1.27 (0.93 to 1.72), with moderate
heterogeneity (I² = 26% and τ² = 0.04).
Adjusted indirect comparisons of placebo-controlled trials
For the two meta-analyses of placebo-controlled comparisons of citalopram (n =
10 trials) and escitalopram (n = 12), we found no substantial heterogeneity across
trials (I² = 0% and τ² = 0.00 for citalopram vs. placebo; I² = 27% and τ² = 0.02 for
escitalopram vs. placebo), Section 6, Additional file 1. Patients and trial
characteristics were similar for the two sets of placebo-controlled trials, see Section
6, Additional file 1.
The proportion of responders was significantly greater with citalopram and
escitalopram than placebo and the two effect sizes were of similar magnitude.
Random-effects combined OR 1.50 (1.27 to 1.78) for citalopram and 1.55 (1.33 to
1.82) for escitalopram. From these estimates, the adjusted indirect comparison OR
for citalopram versus escitalopram was 1.03 (0.82 to 1.30). We found a large
9

inconsistency between the direct and indirect estimates (difference in log ORs 0.44,
corresponding to a ratio of OR of 1.55, P = 0.07, Figure 2. Consequently, we could
not combine the direct and indirect estimates in a network meta-analysis. Moreover,
we could not perform a NNT analysis because of a lack of difference in efficacy
between citalopram and escitalopram.
For each set of placebo-controlled trials, we found no evidence of small-study
effect (See Section 12, Additional file 1; Egger's test P = 0.79 for citalopram vs.
placebo and P = 0.46 for escitalopram vs. placebo).
Concerning acceptability, the proportion of treatment completers was lower with
citalopram and escitalopram than placebo, with similar effect sizes, see Section 13,
Additional file 1, the random-effects combined OR 0.91 (0.75 to 1.10) for citalopram
and 0.89 (0.73 to 1.07) for escitalopram. From these estimates, the adjusted indirect
comparison OR for escitalopram versus citalopram was 0.98 (0.75 to 1.28), with
large inconsistency between the direct and indirect estimates (difference in log ORs
0.26, corresponding to a ratio of OR of 1.30 (P = 0.21), Figure 2. We found no
important heterogeneity in the two sets of trials (I² = 0% and τ² = 0.00 for citalopram
vs. placebo; I² = 25% and τ² = 0.03 for escitalopram vs. placebo).
The sensitivity analyses of the direct and indirect comparisons after excluding
trials without comparable dosages, with imputed outcome data or of older adults only
gave results consistent with those from the primary analyses (data not shown).
Assessment of reimbursement costs for citalopram, its generic drugs and
escitalopram
Sections 7 and 14, Additional file 1 show the evolution of the number of claims for
each drug form. The projected results from the EGB sample showed a substantial
decrease in consumption of citalopram between 2004 (2.1 million claims) and 2006
(0.7 million claims). This decrease was accompanied by an increase of
approximately twice the claims for the generic forms of citalopram during the same
period. Moreover, the new revised-formula escitalopram represented 40% of the
market share in 2006 (1.7 million claims) after it was introduced to the market in April
2005. Between 2006 and 2011, claims for citalopram continued to decrease, to a
lesser extent, and that for the generic forms continued to increase, to peak in 2008,
10

which was followed by a slight decrease up to 2010. However, escitalopram claims
grew even more steeply towards the end of 2010, reaching 5.4 million claims. By the
end of 2010, escitalopram consumption had exceeded that of citalopram and its
generic forms combined (5.4 million claims for escitalopram vs. 0.2 and 1.7 million
for citalopram and its generic forms, respectively) (Sections 7, 16 and 17, Additional
file 1).
Consumption in DDD units showed changes similar to reimbursement results; for
citalopram consumption, the DDD units decreased from 73.9 million in 2004 to 7.6
million in 2010, whereas for escitalopram, the units increased from 15.7 million in
2005 to 193.9 million in 2010. For generic forms of citalopram, the DDD units were
55.2 million in 2005 and slightly increased to 58.8 million in 2010 (Sections 7 and 15,
Additional file 1).
Reimbursement costs reflected the trends in consumption (Figure 3; Section 7,
Additional file 1). The total monthly cost for the drugs was 5.6 million Euros when the
generic forms were introduced into the market. Although the total monthly cost
slightly decreased to 5.2 million Euros when escitalopram was introduced into the
French market, in May 2005, the monthly cost reached 6.1 million Euros a year
subsequently (Figure 4). The cost burden of escitalopram continued to increase, to
reach 96.8 million Euros in 2010, as compared with citalopram, 4.4 million Euros
(Figure 3). For the generic forms of citalopram, the cost was >20 million Euros from
2005 to 2010. Moreover, the reimbursement cost for escitalopram exceeded that of
citalopram and its generic forms combined (Figure 5). Overall, the health cost burden
of the three drug forms reached 120.6 million Euros in 2010 (see Figure 5 and
Section 17, Additional file 1).
Discussion
We performed a large-scale systematic review to evaluate the impact of
evergreening of citalopram with the chiral switch form escitalopram on efficacy,
treatment acceptability and health insurance costs. We found substantial
discrepancy between the direct and indirect comparisons of citalopram and
escitalopram for short-term treatment efficacy and acceptability. The direct
11

comparison showed clinical superiority of escitalopram over citalopram, but the
indirect comparison showed no evidence of difference between the two. Our analysis
of reimbursement costs for a large representative sample of French health insurance
beneficiaries, showed that escitalopram took a large share of the market, with
citalopram substantially lower. The generic forms of citalopram started to gain an
expected share of the market, but the uptake was suppressed by escitalopram
competition. Moreover, escitalopram consumption overcame citalopram and its
generic forms combined.
Network meta-analysis can be used to combine direct and indirect estimates of
efficacy or acceptability. The analysis borrows strength from all the available
evidence and increases statistical power and precision of estimates. However, we
could not perform such a network meta-analysis because of the observed
discrepancy between direct and indirect evidence. A possible explanation for the
discrepancy is dose difference among trials; however, sensitivity analysis for
comparable dosages showed consistent results. Another possible explanation could
be bias in the direct comparison or the indirect comparison [27, 31]. Head-to-head
trials are usually considered the gold standard. However, such trials may favor the
sponsored treatment [33, 34] or the newest treatment [35-37]. In our analysis, most
head-to-head trials, except two, were sponsored by the manufacturer
(Lundbeck/Forest Lab). One of the two trials was sponsored by Arbacom, a Russian
company with potential conflict of interest with the Danish manufacturer Lundbeck
[38]. The trial results contained outlying results, with strong superiority of
escitalopram over citalopram. The second trial was an institutional funded trial [32],
and did not find any evidence of difference between the two drugs. Escitalopram was
superior to citalopram in a network multiple-drug treatments meta-analysis of efficacy
without placebo controlled trials [22]. However, citalopram was superior to
escitalopram, with a statistical non-significance, in an extensive multiple-drug
treatments meta-analysis that considered the entire network of second-generation
antidepressant drugs and including placebo-controlled trials [21].
Our indirect comparison may have been biased. For instance, the characteristics
of citalopram and escitalopram placebo-controlled trials may have greatly differed,
which would have invalidated the adjusted indirect comparison. However, we found
12

no evidence of unequal distribution of potential treatment effect modifiers. Placebo-
controlled trials may have exhibited reporting bias. Nevertheless, this bias is unlikely
because as compared with active comparator trials, placebo-controlled trials are
frequently registered with the FDA, which is considered a gold standard for placebo-
controlled trials in the antidepressant field [39] and when we searched the FDA
database, we identified five trials with unpublished data. As well, the indirect
comparison may have had low statistical power [40]. However, we ensured a
balance in the number of included trials, with at least 10 randomized comparisons for
both citalopram and escitalopram versus placebo.
Our study has some limitations. In the comparative effectiveness analysis, we
assessed treatment acceptability only and did not assess safety outcomes [21, 22].
Our findings could not be generalized to other patent-extended medications using
chiral switch or other indications for citalopram/escitalopram usage. Second, we
examined the EGB sample [29], although the EGB is a representative sample of the
SNIIR-AM database [30]. The EGB data limited our analysis to begin with 2003, so
we were not able to look at the cost and consumption trends earlier than 2003.
Conclusions
We found strong uncertainty about the clinical benefits of escitalopram over
citalopram. Given the likelihood of sponsorship bias for head-to-head trials and the
absence of reporting bias for placebo-controlled trials [41], our adjusted indirect
comparison may be less biased than the direct comparison. However, the market
share of escitalopram increased substantially and suppressed that of the generic
forms, which may have prevented a substantial cost savings for health insurance,
especially if we assumed prescriptions shifted from escitalopram to generic
citalopram. Finally, as evergreened medications are typically launched to the market
before the patent of the original product expires - in the expectation of generic
competition - it might be suitable to base the new product costs on the estimated
price of the generic form, rather than on the current price of the originator. Moreover,
health technology assessment agencies could redefine product innovation to reflect
13

actual added benefits to patients and society. This might effectively make the
evergreened product less cost effective and might help discourage this practice [42].
Abbreviations
CI, confidence interval; DDD, defined daily dose; EGB, Echantillon généraliste de
bénéficiaires; MADRS, Montgomery-Åsberg depression rating scale; NNT, number
needed to treat; OR, odds ratio; RCTs, randomized controlled trials; SNIIR-AM,
French National Health Insurance Inter-regime Information System
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
AA contributed to the study design and performed the literature search, data
collection and data interpretation. AA was also involved in drafting the manuscript.
LT substantially contributed to the design, data collection and data analysis, and was
also involved in the manuscript drafting and revising its intellectual content. GB
contributed in gathering reimbursement data and its analysis, and also critically
revised the manuscript for intellectual content. MD contributed to the conception and
design of the study, and also was involved in the manuscript revision and approving
its intellectual content. PR contributed substantially to the conception, design of the
evergreening study and the interpretation of data, and was involved in revising the
manuscript critically for intellectual content and has given the final approval of the
version to be published. All authors have read and approved the manuscript for
publication.
Acknowledgements
14

We thank Dr. Annie Rudnichi from the Haute Authorité de Santé and Marie
Dalichampt for their help with querying the SNIIR-AM database, Laura Smales
(BioMedEditing, Toronto, Canada) for editing the manuscript.
An ethics statement was not required for this work and no funding was received
for this work, no funding bodies played any role in the design, writing or decision to
publish this manuscript. We had full access to all the data and take responsibility for
the integrity of the data and the accuracy of the analysis.
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Figure 1. Flow diagram of systematic reviews selection (left panel) and
eligible randomized trials (right panel). *Concomitant diseases: depression in
patients with, for example, fibromyalgia and diabetes. **Specific populations:
children, pregnant women. †Specified depression: end-of-life depression,
postpartum depression.
Figure 2. Direct and indirect comparisons of escitalopram and citalopram for
efficacy and acceptability.
Figure 3. Reimbursement costs (monthly reimbursement expenditure in
Euros) between 2003 and 2011 in France.
Figure 4. Total monthly reimbursement cost for escitalopram, citalopram
and its generic forms.
Figure 5. Total monthly reimbursement cost for citalopram and its generic
forms combined versus escitalopram.
Additional files
Title: Additional file 1.
Description: Section 1: Selection criteria and search strategy. Section 2: Methods
and analysis details. Section 3: Selected trials. Section 4: Network analysis of trials
for direct and indirect comparison and the number of trials in each comparison.
Section 5: Characteristics of trials. Section 6: Characteristics of trials across
19

different comparisons. Section 7: Consumption and cost analyses for the citalopram,
generic citalopram, escitalopram from the French national health insurance
information system. Section 8: Meta-analysis for efficacy data of head-to-head trials.
Section 9: Funnel plot for efficacy data for head-to-head trials. Section 10: Meta-
analysis for acceptability data for head-to-head trials. Section 11: Meta-analysis for
efficacy data for placebo-controlled trials. Section 12: Funnel plot for efficacy data
for placebo-controlled trials. Section 13: Meta-analysis for acceptability data for
placebo-controlled trials. Section 14: Consumption levels (monthly no. of
prescriptions) between 2003 and 2011 in France. Section 15: Consumption levels
(monthly defined daily dose (DDD) units) between 2003 and 2011 in France.
Section 16: Consumption levels for escitalopram versus citalopram and its generic
forms combined. Section 17: Total consumption of escitalopram, citalopram and its
generic forms.
20

Favors Escitalopram
Favors Citalopram
Odds ratio
Figure 2
1
5
2
0.5
0.2
Direct
7 head-to-head
trials
Efficacy Acceptability
Indirect
22 placebo-controlled
trials
Favours Escitalopram
Favours Citalopram
Indirect
22 placebo-controlled
trials
Inconsistency test P value = 0.07 Inconsistency test P value = 0.21
Odds ratio
5
2
1
0.5
0.2
Direct
7 head-to-head
trials

Reimbursement amount (euros)
Figure 3
14000000
13000000
12000000
11000000
10000000
9000000
8000000
7000000
6000000
5000000
4000000
3000000
2000000
1000000
0
Jan 2003 Jan 2004 Jan 2005 Jan 2006 Jan 2007 Jan 2008 Jan 2009 Jan 2010 Jan 2011
Year
CITALOPRAM CITALOPRAM GENERIC DRUGS ESCITALOPRAM

Reimbursement amount (euros)
Figure 4
14000000
12000000
10000000
8000000
6000000
4000000
2000000
0
Jan 2003 Jan 2004 Jan 2005 Jan 2006 Jan 2007 Jan 2008 Jan 2009 Jan 2010 Jan 2011
Year

Reimbursement amount (euros)
Figure 5
14000000
13000000
12000000
11000000
10000000
9000000
8000000
7000000
6000000
5000000
4000000
3000000
2000000
1000000
0
Jan 2003 Jan 2004 Jan 2005 Jan 2006 Jan 2007 Jan 2008 Jan 2009 Jan 2010 Jan 2011
Year
CITALOPRAM (AND GENERIC DRUGS) ESCITALOPRAM

Additional files provided with this submission:
Additional file 1: Evergreening additional file.docx, 142K
http://www.biomedcentral.com/imedia/6359683008247391/supp1.docx